Wegaw utilizes geospatial data fusion and machine learning to create digital twins of snow and water resources, enabling accurate monitoring and forecasting for renewable energy asset optimization. This technology reduces forecasting errors by up to 50% and increases energy generation by 10%, directly addressing the challenges of resource management in the energy sector.
Funding
$2.7M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Renewable energy and utilities asset managers face challenges in accurately monitoring and forecasting water and snow resources, leading to inefficiencies in energy trading and generation. Traditional methods often lack the precision and real-time insights needed to optimize asset performance and adapt to changing environmental conditions.
Solution
Wegaw provides digital twins of snow and water resources through geospatial data fusion and machine learning, enabling accurate monitoring and forecasting for renewable energy asset optimization. The platform combines multiple geospatial data inputs with machine learning to deliver historical and near real-time analysis of snow and water data. This technology helps companies optimize assets by improving water inflow models, optimizing energy trading, and increasing energy generation. Wegaw's SaaS-based solution eliminates the need for in-situ hardware and offers scalable, secure, and near real-time model calibration.
Target Audience
The primary customers are utilities, asset managers, and energy producers seeking to optimize resources, increase trading efficiencies, and reduce the cost of data acquisition.
Features
- Combines geospatial data inputs with machine learning for historical and near real-time analysis
- Provides daily snow cover extent (SCE), snow height (HS), and snow water equivalent (SWE) maps at resolutions up to 2m
- Calibrates water inflow models with Snow Water Equivalent (SWE) for error reduction in water forecasting
- Offers snow height maps derived through the subtraction of a ‘snow-on’ Digital Surface Model (DSM) from a ‘snow off’ DSM
- Integrates in-situ Ultrasound HS measurements and GNSS-derived HS
- Utilizes optical satellite imagery from ESA Copernicus Sentinel-2 & NASA/NOAA MODIS/VIIRS
- Incorporates atmospheric data products such as the NOAA Global Forecast System GFS or ECMWF Reanalysis v5 (ERA5)
- Employs a generalized additive model (GAM) to interpolate between station networks and satellite data